VANET clustering solves scalability issues while fortifying the network and extending its lifespan. The following phases are included in the developed work on Energy-Efficient Cluster-based Routing (EECR) and Fuzzy-To...
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This paper presents a hybrid machine-learning framework for optimizing 3-Dimensional (3D) Unmanned Aerial Vehicles (UAV) node localization and resource distribution in UAV-assisted THz 6G networks to ensure efficient ...
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Phonocardiogram (PCG) signal is the digital sound recording of various heart sounds. To diagnose the different types of heart disorders, it is often necessary to analyse these PCG signals. However, PCG signal recordin...
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The present research evaluates optical angular momentum's (OAM) performance in challenging atmospheric conditions and emphasizes its significance in free space optical (FSO) communication systems. It has been demo...
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It is difficult to improve both energy consumption and detection accuracy simultaneously, and even to obtain the trade-off between them, when detecting and tracking moving targets, especially for Underwater Wireless S...
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It is difficult to improve both energy consumption and detection accuracy simultaneously, and even to obtain the trade-off between them, when detecting and tracking moving targets, especially for Underwater Wireless Sensor Networks(UWSNs). To this end, this paper investigates the relationship between the Degree of Target Change(DoTC) and the detection period, as well as the impact of individual nodes. A Hierarchical Detection and Tracking Approach(HDTA) is proposed. Firstly, the network detection period is determined according to DoTC, which reflects the variation of target motion. Secondly, during the network detection period, each detection node calculates its own node detection period based on the detection mutual information. Taking DoTC as pheromone, an ant colony algorithm is proposed to adaptively adjust the network detection period. The simulation results show that the proposed HDTA with the optimizations of network level and node level significantly improves the detection accuracy by 25% and the network energy consumption by 10% simultaneously, compared to the traditional adaptive period detection schemes.
The rapid industrial growth and increasing population have led to significant pollution and deterioration of the natural atmospheric *** atmospheric pollutants include NO_(2)and CO_(2).Hence,it is imperative to develo...
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The rapid industrial growth and increasing population have led to significant pollution and deterioration of the natural atmospheric *** atmospheric pollutants include NO_(2)and CO_(2).Hence,it is imperative to develop NO_(2)and CO_(2)sensors for ambient conditions,that can be used in indoor air quality monitoring,breath analysis,food spoilage detection,*** the present study,two thin film nanocomposite(nickel oxide-graphene and nickel oxide-silver nanowires)gas sensors are fabricated using direct ink *** nano-composites are investigated for their structural,optical,and electrical *** the nano-composite is deposited on the interdigitated electrode(IDE)pattern to form NO_(2)and CO_(2)*** deposited films are then exposed to NO_(2)and CO_(2)gases separately and their response and recovery times are determined using a custom-built gas sensing *** oxide-graphene provides a good response time and recovery time of 10 and 9 s,respectively for NO_(2),due to the higher electron affinity of graphene towards NO_(2).Nickel oxide-silver nanowire nano-composite is suited for CO_(2)gas because silver is an excellent electrocatalyst for CO_(2)by giving response and recovery times of 11 s *** is the first report showcasing NiO nano-composites for NO_(2)and CO_(2)sensing at room temperature.
Accurate crowd counting in natural images has become increasingly attractive owing to its numerous real-world applications, e.g., crowd analysis and video surveillance. Despite significant progress in crowd counting [...
Accurate crowd counting in natural images has become increasingly attractive owing to its numerous real-world applications, e.g., crowd analysis and video surveillance. Despite significant progress in crowd counting [1,2], challenges(such as scale variation and background clutter) *** fully utilize spatial information, existing crowd counting approaches [3, 4] mainly estimate a density map, where point annotations are smoothed via a Gaussian kernel to generate probabilities indicating the presence of a crowd.
Pancreatic cancer is one of the deadliest cancers with high mortality rates as it is often diagnosed late, leading to limited treatment options. This demands an effective classification algorithm to accurately detect ...
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Human action recognition is a vital aspect of computer vision, with applications ranging from security systems to interactive technology. Our study presents a comprehensive methodology that employs multiple feature ex...
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Human action recognition is a vital aspect of computer vision, with applications ranging from security systems to interactive technology. Our study presents a comprehensive methodology that employs multiple feature extraction and optimization techniques to enhance the accuracy and efficiency of human action identification. The video input was divided into four distinct elements: RGB images, optical flow information, spatial saliency maps, and temporal saliency maps. Each component was analyzed independently using advanced computer vision algorithms. The process involves utilizing various algorithms and techniques to extract meaningful information from the visual data. The Farneback algorithm was employed to examine the optical flow, whereas Canny edge detection was used to assess spatial prominence. Additionally, frame comparison helps to identify motion-based prominence. These processed elements provide a comprehensive representation of both spatial and temporal information. The extracted data were then input into distinct pretrained deep learning models. Specifically, Inception V3 was used for RGB frames and optical flow analysis, ResNetV2 processed spatial saliency maps, and DenseNet-121 handled motion saliency maps. The input data are processed separately by these networks, each of which extracts specific features that are suited to their respective modalities. This feature extraction process ensures the comprehensive capture of both static and dynamic elements in video data. Subsequently, sequence modeling and classification were performed using a gated recurrent unit (GRU) that incorporated an attention mechanism. This mechanism dynamically highlights the most significant temporal segments, improving the capacity of the model to comprehend intricate human actions within video sequences. To enhance the efficiency of the model, we implemented the Grasshopper optimization algorithm to optimize the feature selection and classification stages, thus maximizing the u
A wide solution to address the growing diversity of specialized security threats in healthcare information management, watermarking has been proposed in response to contemporary worries about multimedia security and t...
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